Question-answering (QA) research for questions related to some facts, a so-called factoid question, has recently achieved great success. Recently, question-answering systems have been remarkably improved as demonstrated by IBM's Watson, Apple's Siri and so on, and some systems have already been commercially used. Similar developments are made by companies other than those mentioned above. On factoid questions, accuracy of such systems is reported to be about 85%.
On the other hand, why-type question-answering, a task to extract an answer or answers to a question asking a reason why some event occurs, such as “why we get cancer?” has been recognized as far more difficult than to answer a factoid question. The products of IBM and Apple mentioned above do not handle why-type questions.
In this regard, Non-Patent Literature 1 cited below discloses a so-called information retrieval technique in which a word such as “reason” is added to a query of information retrieval to find a passage including an answer to a given why-type question from a huge amount of documents. Non-Patent Literature 2 discloses a technique of specifying an answer through supervised learning, using, as features, word pairs and patterns appearing in a manually prepared database of word pairs representing causes and results, or in a manually prepared database of syntax patterns representing reasons.
Separate from above, Non-Patent Literature 3 discloses a technique of specifying an answer through supervised learning, using, as features including morpho-syntactic features such as morphological features, that is n-grams of morphemes and their part-of-speech tags, and structural features of texts, that is, partial syntactic trees, and semantic features such as semantic classes of words, evaluation expressions.